How Dash uses context engineering for smarter AI

This post details how Dropbox's Dash product is evolving from a traditional RAG pipeline to an agentic AI system by focusing on 'context engineering'. Key technical contributions include: 1. **Retrieval Consolidation:** Replacing multiple retrieval tools with a single, purpose-built tool backed by the Dash universal search index to simplify the model's decision space. 2. **Context Filtering with Knowledge Graph:** Building a unified index and layering a knowledge graph to connect people, activity, and content, enabling relevance-based ranking of retrieved information. 3. **Specialized Agents:** Moving complex tools like query construction into dedicated agents with focused prompts to allow the main planning agent to focus on higher-level execution. The post also highlights the use of the Model Context Protocol (MCP) and the development of a Dash MCP server for integrating with other apps.